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Volumn , Issue , 2011, Pages 994-999

Learning recurring concepts from data streams with a context-aware ensemble

Author keywords

[No Author keywords available]

Indexed keywords

CHANGE DETECTION; CONTEXT INFORMATION; CONTEXT-AWARE; DATA STREAM; DATA STREAM MINING; LEARNING PROCESS; REAL DATA SETS; REAL-WORLD APPLICATION; RESOURCE AVAILABILITY; RESOURCE CONSTRAINT;

EID: 79959292611     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1982185.1982403     Document Type: Conference Paper
Times cited : (41)

References (18)
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  • 2
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  • 3
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    • series: chapman & hall/crc data mining and knowledge discovery series
    • J. Gama. Knowledge discovery from data streams (series: chapman & hall/crc data mining and knowledge discovery series). 2010.
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    • Gama, J.1
  • 6
    • 0032139819 scopus 로고    scopus 로고
    • Extracting hidden context
    • DOI 10.1023/A:1007420529897
    • M. Harries, C. Sammut, and K. Horn. Extracting hidden context. Machine Learning, 32(2):101-126, 1998. (Pubitemid 40626077)
    • (1998) Machine Learning , vol.32 , Issue.2 , pp. 101-126
    • Harries, M.B.1    Sammut, C.2    Horn, K.3
  • 8
    • 77956234457 scopus 로고    scopus 로고
    • Tracking recurring contexts using ensemble classifiers: An application to email filtering
    • I. Katakis, G. Tsoumakas, and I. Vlahavas. Tracking recurring contexts using ensemble classifiers: an application to email filtering. Knowledge and Information Systems, pages 1-21.
    • Knowledge and Information Systems , pp. 1-21
    • Katakis, I.1    Tsoumakas, G.2    Vlahavas, I.3
  • 9
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    • Dynamic weighted majority: An ensemble method for drifting concepts
    • J. Kolter and M. Maloof. Dynamic weighted majority: An ensemble method for drifting concepts. The Journal of Machine Learning Research, 8:2755-2790, 2007.
    • (2007) The Journal of Machine Learning Research , vol.8 , pp. 2755-2790
    • Kolter, J.1    Maloof, M.2
  • 12
    • 0035788947 scopus 로고    scopus 로고
    • A streaming ensemble algorithm (SEA) for large-scale classification
    • ACM New York, NY, USA
    • W. Street and Y. Kim. A streaming ensemble algorithm (SEA) for large-scale classification. In Proceedings of the seventh ACM SIGKDD, pages 377-382. ACM New York, NY, USA, 2001.
    • (2001) Proceedings of the Seventh ACM SIGKDD , pp. 377-382
    • Street, W.1    Kim, Y.2
  • 16
    • 0031164523 scopus 로고    scopus 로고
    • Tracking Context Changes through Meta-Learning
    • G. Widmer. Tracking context changes through meta-learning. Machine Learning, 27(3):259-286, 1997. (Pubitemid 127510031)
    • (1997) Machine Learning , vol.27 , Issue.3 , pp. 259-286
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  • 17
    • 0030126609 scopus 로고    scopus 로고
    • Learning in the presence of concept drift and hidden contexts
    • G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine learning, 23(1):69-101, 1996. (Pubitemid 126737384)
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    • Widmer, G.1
  • 18
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    • Mining in anticipation for concept change: Proactive-reactive prediction in data streams
    • DOI 10.1007/s10618-006-0050-x
    • Y. Yang, X. Wu, and X. Zhu. Mining in anticipation for concept change: Proactive-reactive prediction in data streams. Data mining and knowledge discovery, 13(3):261-289, 2006. (Pubitemid 44455021)
    • (2006) Data Mining and Knowledge Discovery , vol.13 , Issue.3 , pp. 261-289
    • Yang, Y.1    Wu, X.2    Zhu, X.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.